Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations8992
Missing cells14540
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 MiB
Average record size in memory919.7 B

Variable types

Numeric8
Categorical6
Text6
DateTime1

Alerts

Rank has constant value "-" Constant
Ano/Mes is highly overall correlated with Fase and 1 other fieldsHigh correlation
Canal is highly overall correlated with CategoriaHigh correlation
Categoria is highly overall correlated with Canal and 1 other fieldsHigh correlation
Fase is highly overall correlated with Ano/Mes and 1 other fieldsHigh correlation
Join_WhatsApp is highly overall correlated with Leads and 1 other fieldsHigh correlation
Leads is highly overall correlated with Join_WhatsApp and 1 other fieldsHigh correlation
Leads Tráfego Pago is highly overall correlated with Categoria and 4 other fieldsHigh correlation
Organizador is highly overall correlated with Ano/Mes and 1 other fieldsHigh correlation
Receita is highly overall correlated with VendasHigh correlation
Sessions is highly overall correlated with Leads Tráfego Pago and 1 other fieldsHigh correlation
Total users is highly overall correlated with Leads Tráfego Pago and 1 other fieldsHigh correlation
Vendas is highly overall correlated with ReceitaHigh correlation
Objetivo is highly imbalanced (80.4%) Imbalance
Fase has 360 (4.0%) missing values Missing
Investimento has 6208 (69.0%) missing values Missing
Leads Tráfego Pago has 7972 (88.7%) missing values Missing
Organizador is uniformly distributed Uniform
Organizador has unique values Unique
Join_WhatsApp has 7237 (80.5%) zeros Zeros
Leads has 7331 (81.5%) zeros Zeros
Vendas has 8568 (95.3%) zeros Zeros
Receita has 8568 (95.3%) zeros Zeros
Leads Tráfego Pago has 703 (7.8%) zeros Zeros

Reproduction

Analysis started2025-03-18 17:11:15.236765
Analysis finished2025-03-18 17:11:17.585703
Duration2.35 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Organizador
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct8992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4496.5
Minimum1
Maximum8992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-03-18T14:11:17.608236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile450.55
Q12248.75
median4496.5
Q36744.25
95-th percentile8542.45
Maximum8992
Range8991
Interquartile range (IQR)4495.5

Descriptive statistics

Standard deviation2595.9111
Coefficient of variation (CV)0.57731817
Kurtosis-1.2
Mean4496.5
Median Absolute Deviation (MAD)2248
Skewness0
Sum40432528
Variance6738754.7
MonotonicityStrictly increasing
2025-03-18T14:11:17.640741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5998 1
 
< 0.1%
5992 1
 
< 0.1%
5993 1
 
< 0.1%
5994 1
 
< 0.1%
5995 1
 
< 0.1%
5996 1
 
< 0.1%
5997 1
 
< 0.1%
5999 1
 
< 0.1%
5990 1
 
< 0.1%
Other values (8982) 8982
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8992 1
< 0.1%
8991 1
< 0.1%
8990 1
< 0.1%
8989 1
< 0.1%
8988 1
< 0.1%
8987 1
< 0.1%
8986 1
< 0.1%
8985 1
< 0.1%
8984 1
< 0.1%
8983 1
< 0.1%

Ano/Mes
Categorical

High correlation 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size584.6 KiB
2024 | Março
891 
2024 | Setembro
826 
2024 | Agosto
762 
2025 | Janeiro
718 
2024 | Novembro
714 
Other values (10)
5081 

Length

Max length16
Median length15
Mean length13.527691
Min length11

Characters and Unicode

Total characters121641
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024 | Janeiro
2nd row2024 | Janeiro
3rd row2024 | Janeiro
4th row2024 | Janeiro
5th row2024 | Janeiro

Common Values

ValueCountFrequency (%)
2024 | Março 891
9.9%
2024 | Setembro 826
9.2%
2024 | Agosto 762
 
8.5%
2025 | Janeiro 718
 
8.0%
2024 | Novembro 714
 
7.9%
2024 | Outubro 696
 
7.7%
2024 | Dezembro 638
 
7.1%
2024 | Abril 619
 
6.9%
2024 | Julho 617
 
6.9%
2025 | Fevereiro 596
 
6.6%
Other values (5) 1915
21.3%

Length

2025-03-18T14:11:17.674459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8992
33.3%
2024 7360
27.3%
2025 1632
 
6.0%
março 1209
 
4.5%
fevereiro 947
 
3.5%
janeiro 907
 
3.4%
setembro 826
 
3.1%
agosto 762
 
2.8%
novembro 714
 
2.6%
outubro 696
 
2.6%
Other values (5) 2931
 
10.9%

Most occurring characters

ValueCountFrequency (%)
2 17984
14.8%
17984
14.8%
o 9849
 
8.1%
| 8992
 
7.4%
0 8992
 
7.4%
r 7503
 
6.2%
e 7390
 
6.1%
4 7360
 
6.1%
b 3493
 
2.9%
i 3026
 
2.5%
Other values (21) 29068
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 17984
14.8%
17984
14.8%
o 9849
 
8.1%
| 8992
 
7.4%
0 8992
 
7.4%
r 7503
 
6.2%
e 7390
 
6.1%
4 7360
 
6.1%
b 3493
 
2.9%
i 3026
 
2.5%
Other values (21) 29068
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 17984
14.8%
17984
14.8%
o 9849
 
8.1%
| 8992
 
7.4%
0 8992
 
7.4%
r 7503
 
6.2%
e 7390
 
6.1%
4 7360
 
6.1%
b 3493
 
2.9%
i 3026
 
2.5%
Other values (21) 29068
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 17984
14.8%
17984
14.8%
o 9849
 
8.1%
| 8992
 
7.4%
0 8992
 
7.4%
r 7503
 
6.2%
e 7390
 
6.1%
4 7360
 
6.1%
b 3493
 
2.9%
i 3026
 
2.5%
Other values (21) 29068
23.9%
Distinct3742
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Memory size647.9 KiB
2025-03-18T14:11:17.742792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length13
Mean length15.099422
Min length8

Characters and Unicode

Total characters135774
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1584 ?
Unique (%)17.6%

Sample

1st rowTráfego Direto45307
2nd rowTráfego Direto45308
3rd rowMeta Ads45308
4th rowReferral45308
5th rowMeta Ads45308
ValueCountFrequency (%)
linkedin 1054
 
8.3%
meta 810
 
6.4%
instagram 710
 
5.6%
e-mail 461
 
3.6%
tráfego 424
 
3.3%
google 210
 
1.7%
referral45364 92
 
0.7%
referral45365 67
 
0.5%
referral45363 65
 
0.5%
referral45368 63
 
0.5%
Other values (3176) 8705
68.8%
2025-03-18T14:11:17.834528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 13123
 
9.7%
4 12604
 
9.3%
e 10387
 
7.7%
r 9827
 
7.2%
a 6855
 
5.0%
n 5043
 
3.7%
6 4398
 
3.2%
i 4164
 
3.1%
o 4115
 
3.0%
t 3951
 
2.9%
Other values (39) 61307
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 13123
 
9.7%
4 12604
 
9.3%
e 10387
 
7.7%
r 9827
 
7.2%
a 6855
 
5.0%
n 5043
 
3.7%
6 4398
 
3.2%
i 4164
 
3.1%
o 4115
 
3.0%
t 3951
 
2.9%
Other values (39) 61307
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 13123
 
9.7%
4 12604
 
9.3%
e 10387
 
7.7%
r 9827
 
7.2%
a 6855
 
5.0%
n 5043
 
3.7%
6 4398
 
3.2%
i 4164
 
3.1%
o 4115
 
3.0%
t 3951
 
2.9%
Other values (39) 61307
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 13123
 
9.7%
4 12604
 
9.3%
e 10387
 
7.7%
r 9827
 
7.2%
a 6855
 
5.0%
n 5043
 
3.7%
6 4398
 
3.2%
i 4164
 
3.1%
o 4115
 
3.0%
t 3951
 
2.9%
Other values (39) 61307
45.2%

Categoria
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size836.9 KiB
Tráfego Orgânico
4168 
Social Orgânico
3318 
Tráfego Pago
1020 
CRM
461 
Hello Bar
 
17

Length

Max length16
Median length15
Mean length14.487767
Min length3

Characters and Unicode

Total characters130274
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTráfego Orgânico
2nd rowTráfego Orgânico
3rd rowTráfego Pago
4th rowTráfego Orgânico
5th rowTráfego Pago

Common Values

ValueCountFrequency (%)
Tráfego Orgânico 4168
46.4%
Social Orgânico 3318
36.9%
Tráfego Pago 1020
 
11.3%
CRM 461
 
5.1%
Hello Bar 17
 
0.2%
Teste 8
 
0.1%

Length

2025-03-18T14:11:17.862289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-18T14:11:17.886544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
orgânico 7486
42.7%
tráfego 5188
29.6%
social 3318
18.9%
pago 1020
 
5.8%
crm 461
 
2.6%
hello 17
 
0.1%
bar 17
 
0.1%
teste 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 17029
13.1%
g 13694
10.5%
r 12691
9.7%
c 10804
 
8.3%
i 10804
 
8.3%
8523
 
6.5%
n 7486
 
5.7%
O 7486
 
5.7%
â 7486
 
5.7%
e 5221
 
4.0%
Other values (14) 29050
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 17029
13.1%
g 13694
10.5%
r 12691
9.7%
c 10804
 
8.3%
i 10804
 
8.3%
8523
 
6.5%
n 7486
 
5.7%
O 7486
 
5.7%
â 7486
 
5.7%
e 5221
 
4.0%
Other values (14) 29050
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 17029
13.1%
g 13694
10.5%
r 12691
9.7%
c 10804
 
8.3%
i 10804
 
8.3%
8523
 
6.5%
n 7486
 
5.7%
O 7486
 
5.7%
â 7486
 
5.7%
e 5221
 
4.0%
Other values (14) 29050
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 17029
13.1%
g 13694
10.5%
r 12691
9.7%
c 10804
 
8.3%
i 10804
 
8.3%
8523
 
6.5%
n 7486
 
5.7%
O 7486
 
5.7%
â 7486
 
5.7%
e 5221
 
4.0%
Other values (14) 29050
22.3%

Canal
Categorical

High correlation 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size593.3 KiB
Referral
3014 
LinkedIn Orgânico
1054 
Youtube
894 
Meta Ads
810 
Instagram Orgânico
710 
Other values (10)
2510 

Length

Max length18
Median length8
Mean length10.099422
Min length3

Characters and Unicode

Total characters90814
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTráfego Direto
2nd rowTráfego Direto
3rd rowMeta Ads
4th rowReferral
5th rowMeta Ads

Common Values

ValueCountFrequency (%)
Referral 3014
33.5%
LinkedIn Orgânico 1054
 
11.7%
Youtube 894
 
9.9%
Meta Ads 810
 
9.0%
Instagram Orgânico 710
 
7.9%
WhatsApp 644
 
7.2%
SEO 638
 
7.1%
E-mail Marketing 461
 
5.1%
Tráfego Direto 424
 
4.7%
Google Ads 210
 
2.3%
Other values (5) 133
 
1.5%

Length

2025-03-18T14:11:17.915033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
referral 3014
23.8%
orgânico 1764
13.9%
linkedin 1054
 
8.3%
ads 1020
 
8.1%
youtube 894
 
7.1%
meta 810
 
6.4%
instagram 710
 
5.6%
whatsapp 644
 
5.1%
seo 638
 
5.0%
marketing 461
 
3.6%
Other values (9) 1652
13.0%

Most occurring characters

ValueCountFrequency (%)
e 10387
 
11.4%
r 9827
 
10.8%
a 6855
 
7.5%
n 5043
 
5.6%
i 4164
 
4.6%
o 4115
 
4.5%
t 3951
 
4.4%
l 3713
 
4.1%
3669
 
4.0%
g 3602
 
4.0%
Other values (29) 35488
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10387
 
11.4%
r 9827
 
10.8%
a 6855
 
7.5%
n 5043
 
5.6%
i 4164
 
4.6%
o 4115
 
4.5%
t 3951
 
4.4%
l 3713
 
4.1%
3669
 
4.0%
g 3602
 
4.0%
Other values (29) 35488
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10387
 
11.4%
r 9827
 
10.8%
a 6855
 
7.5%
n 5043
 
5.6%
i 4164
 
4.6%
o 4115
 
4.5%
t 3951
 
4.4%
l 3713
 
4.1%
3669
 
4.0%
g 3602
 
4.0%
Other values (29) 35488
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10387
 
11.4%
r 9827
 
10.8%
a 6855
 
7.5%
n 5043
 
5.6%
i 4164
 
4.6%
o 4115
 
4.5%
t 3951
 
4.4%
l 3713
 
4.1%
3669
 
4.0%
g 3602
 
4.0%
Other values (29) 35488
39.1%

Concat
Text

Distinct600
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size616.5 KiB
2025-03-18T14:11:17.977756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length76
Median length42
Mean length21.192949
Min length6

Characters and Unicode

Total characters190567
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique425 ?
Unique (%)4.7%

Sample

1st row(direct)(none)
2nd row(direct)(none)
3rd rowfacebooklinktext
4th rowmy.orbitpages.comreferral
5th rowfacebookFacebook_Right_Column
ValueCountFrequency (%)
direct)(none 424
 
4.2%
linkedin.comreferral 420
 
4.1%
googleorganic 397
 
3.9%
set 368
 
3.6%
youtube.comreferral 354
 
3.5%
jornadadedados2024.com.brreferral 317
 
3.1%
facebookcpc-leads 307
 
3.0%
not 284
 
2.8%
lnkd.inreferral 282
 
2.8%
l.instagram.comreferral 280
 
2.8%
Other values (597) 6735
66.2%
2025-03-18T14:11:18.076138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 19419
 
10.2%
e 18331
 
9.6%
a 18099
 
9.5%
o 16414
 
8.6%
c 10642
 
5.6%
l 9321
 
4.9%
n 8452
 
4.4%
i 7661
 
4.0%
s 7547
 
4.0%
d 7494
 
3.9%
Other values (62) 67187
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 190567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 19419
 
10.2%
e 18331
 
9.6%
a 18099
 
9.5%
o 16414
 
8.6%
c 10642
 
5.6%
l 9321
 
4.9%
n 8452
 
4.4%
i 7661
 
4.0%
s 7547
 
4.0%
d 7494
 
3.9%
Other values (62) 67187
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 190567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 19419
 
10.2%
e 18331
 
9.6%
a 18099
 
9.5%
o 16414
 
8.6%
c 10642
 
5.6%
l 9321
 
4.9%
n 8452
 
4.4%
i 7661
 
4.0%
s 7547
 
4.0%
d 7494
 
3.9%
Other values (62) 67187
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 190567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 19419
 
10.2%
e 18331
 
9.6%
a 18099
 
9.5%
o 16414
 
8.6%
c 10642
 
5.6%
l 9321
 
4.9%
n 8452
 
4.4%
i 7661
 
4.0%
s 7547
 
4.0%
d 7494
 
3.9%
Other values (62) 67187
35.3%

Semana
Text

Distinct62
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size606.0 KiB
2025-03-18T14:11:18.149131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters179840
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024 | 15/01 - 21/01
2nd row2024 | 15/01 - 21/01
3rd row2024 | 15/01 - 21/01
4th row2024 | 15/01 - 21/01
5th row2024 | 15/01 - 21/01
ValueCountFrequency (%)
17984
40.0%
2024 7360
16.4%
2025 1632
 
3.6%
11/03 501
 
1.1%
17/03 501
 
1.1%
10/03 257
 
0.6%
15/09 226
 
0.5%
09/09 226
 
0.5%
03/03 219
 
0.5%
02/09 198
 
0.4%
Other values (113) 15856
35.3%
2025-03-18T14:11:18.243904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35968
20.0%
0 30920
17.2%
2 28713
16.0%
/ 17984
10.0%
1 16037
8.9%
4 10157
 
5.6%
| 8992
 
5.0%
- 8992
 
5.0%
3 4768
 
2.7%
5 4433
 
2.5%
Other values (4) 12876
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 179840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35968
20.0%
0 30920
17.2%
2 28713
16.0%
/ 17984
10.0%
1 16037
8.9%
4 10157
 
5.6%
| 8992
 
5.0%
- 8992
 
5.0%
3 4768
 
2.7%
5 4433
 
2.5%
Other values (4) 12876
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 179840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35968
20.0%
0 30920
17.2%
2 28713
16.0%
/ 17984
10.0%
1 16037
8.9%
4 10157
 
5.6%
| 8992
 
5.0%
- 8992
 
5.0%
3 4768
 
2.7%
5 4433
 
2.5%
Other values (4) 12876
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 179840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35968
20.0%
0 30920
17.2%
2 28713
16.0%
/ 17984
10.0%
1 16037
8.9%
4 10157
 
5.6%
| 8992
 
5.0%
- 8992
 
5.0%
3 4768
 
2.7%
5 4433
 
2.5%
Other values (4) 12876
 
7.2%

Fase
Categorical

High correlation  Missing 

Distinct38
Distinct (%)0.4%
Missing360
Missing (%)4.0%
Memory size844.7 KiB
Ongoing
2087 
2º Lançamento | Vendas
 
470
6º Lançamento | Leads
 
376
12º Lançamento | Leads
 
368
9º Lançamento | Leads
 
343
Other values (33)
4988 

Length

Max length23
Median length22
Mean length18.072405
Min length7

Characters and Unicode

Total characters156001
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOngoing
2nd rowOngoing
3rd rowOngoing
4th rowOngoing
5th rowOngoing

Common Values

ValueCountFrequency (%)
Ongoing 2087
23.2%
2º Lançamento | Vendas 470
 
5.2%
6º Lançamento | Leads 376
 
4.2%
12º Lançamento | Leads 368
 
4.1%
9º Lançamento | Leads 343
 
3.8%
7º Lançamento | Leads 321
 
3.6%
14º Lançamento | Leads 318
 
3.5%
4º Lançamento | Leads 303
 
3.4%
13º Lançamento | Leads 294
 
3.3%
8º Lançamento | Leads 275
 
3.1%
Other values (28) 3477
38.7%
(Missing) 360
 
4.0%

Length

2025-03-18T14:11:18.272720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lançamento 6545
23.2%
6545
23.2%
leads 3594
12.7%
vendas 2337
 
8.3%
ongoing 2087
 
7.4%
741
 
2.6%
live 614
 
2.2%
596
 
2.1%
593
 
2.1%
590
 
2.1%
Other values (9) 4025
14.2%

Most occurring characters

ValueCountFrequency (%)
19635
12.6%
n 19601
12.6%
a 19021
12.2%
e 13090
 
8.4%
L 10753
 
6.9%
o 8632
 
5.5%
t 6545
 
4.2%
m 6545
 
4.2%
ç 6545
 
4.2%
| 6545
 
4.2%
Other values (18) 39089
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19635
12.6%
n 19601
12.6%
a 19021
12.2%
e 13090
 
8.4%
L 10753
 
6.9%
o 8632
 
5.5%
t 6545
 
4.2%
m 6545
 
4.2%
ç 6545
 
4.2%
| 6545
 
4.2%
Other values (18) 39089
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19635
12.6%
n 19601
12.6%
a 19021
12.2%
e 13090
 
8.4%
L 10753
 
6.9%
o 8632
 
5.5%
t 6545
 
4.2%
m 6545
 
4.2%
ç 6545
 
4.2%
| 6545
 
4.2%
Other values (18) 39089
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19635
12.6%
n 19601
12.6%
a 19021
12.2%
e 13090
 
8.4%
L 10753
 
6.9%
o 8632
 
5.5%
t 6545
 
4.2%
m 6545
 
4.2%
ç 6545
 
4.2%
| 6545
 
4.2%
Other values (18) 39089
25.1%

Objetivo
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size441.5 KiB
-
8467 
Leads
 
307
Vendas
 
189
Awareness
 
29

Length

Max length9
Median length1
Mean length1.26746
Min length1

Characters and Unicode

Total characters11397
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 8467
94.2%
Leads 307
 
3.4%
Vendas 189
 
2.1%
Awareness 29
 
0.3%

Length

2025-03-18T14:11:18.297714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-18T14:11:18.315201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8467
94.2%
leads 307
 
3.4%
vendas 189
 
2.1%
awareness 29
 
0.3%

Most occurring characters

ValueCountFrequency (%)
- 8467
74.3%
e 554
 
4.9%
s 554
 
4.9%
a 525
 
4.6%
d 496
 
4.4%
L 307
 
2.7%
n 218
 
1.9%
V 189
 
1.7%
A 29
 
0.3%
w 29
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 8467
74.3%
e 554
 
4.9%
s 554
 
4.9%
a 525
 
4.6%
d 496
 
4.4%
L 307
 
2.7%
n 218
 
1.9%
V 189
 
1.7%
A 29
 
0.3%
w 29
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 8467
74.3%
e 554
 
4.9%
s 554
 
4.9%
a 525
 
4.6%
d 496
 
4.4%
L 307
 
2.7%
n 218
 
1.9%
V 189
 
1.7%
A 29
 
0.3%
w 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 8467
74.3%
e 554
 
4.9%
s 554
 
4.9%
a 525
 
4.6%
d 496
 
4.4%
L 307
 
2.7%
n 218
 
1.9%
V 189
 
1.7%
A 29
 
0.3%
w 29
 
0.3%

Date
Date

Distinct426
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
Minimum2024-01-16 00:00:00
Maximum2025-03-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-18T14:11:18.339850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:18.373315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct518
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size540.7 KiB
2025-03-18T14:11:18.438954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length68
Median length47
Mean length12.555716
Min length2

Characters and Unicode

Total characters112901
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique397 ?
Unique (%)4.4%

Sample

1st row(direct)
2nd row(direct)
3rd rowfacebook
4th rowmy.orbitpages.com
5th rowfacebook
ValueCountFrequency (%)
whatsapp 754
 
7.7%
facebook 709
 
7.2%
google 607
 
6.2%
youtube 494
 
5.0%
direct 424
 
4.3%
linkedin.com 421
 
4.3%
youtube.com 354
 
3.6%
linkedin 319
 
3.3%
jornadadedados2024.com.br 317
 
3.2%
lnkd.in 284
 
2.9%
Other values (513) 5117
52.2%
2025-03-18T14:11:18.534963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 11588
 
10.3%
a 9839
 
8.7%
e 6681
 
5.9%
d 6247
 
5.5%
c 6136
 
5.4%
. 6000
 
5.3%
t 5659
 
5.0%
s 5107
 
4.5%
m 5042
 
4.5%
n 4926
 
4.4%
Other values (60) 45676
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112901
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 11588
 
10.3%
a 9839
 
8.7%
e 6681
 
5.9%
d 6247
 
5.5%
c 6136
 
5.4%
. 6000
 
5.3%
t 5659
 
5.0%
s 5107
 
4.5%
m 5042
 
4.5%
n 4926
 
4.4%
Other values (60) 45676
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112901
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 11588
 
10.3%
a 9839
 
8.7%
e 6681
 
5.9%
d 6247
 
5.5%
c 6136
 
5.4%
. 6000
 
5.3%
t 5659
 
5.0%
s 5107
 
4.5%
m 5042
 
4.5%
n 4926
 
4.4%
Other values (60) 45676
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112901
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 11588
 
10.3%
a 9839
 
8.7%
e 6681
 
5.9%
d 6247
 
5.5%
c 6136
 
5.4%
. 6000
 
5.3%
t 5659
 
5.0%
s 5107
 
4.5%
m 5042
 
4.5%
n 4926
 
4.4%
Other values (60) 45676
40.5%
Distinct77
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size506.3 KiB
2025-03-18T14:11:18.590098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length33
Mean length8.6372331
Min length2

Characters and Unicode

Total characters77666
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row(none)
2nd row(none)
3rd rowlinktext
4th rowreferral
5th rowFacebook_Right_Column
ValueCountFrequency (%)
referral 4255
45.5%
organic 872
 
9.3%
email 431
 
4.6%
none 424
 
4.5%
social-organico 388
 
4.1%
not 368
 
3.9%
set 368
 
3.9%
cpc-leads 307
 
3.3%
cpc 247
 
2.6%
grupos-descricao 194
 
2.1%
Other values (68) 1506
 
16.1%
2025-03-18T14:11:18.675688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 15231
19.6%
e 11650
15.0%
a 8260
10.6%
l 6100
7.9%
o 4826
 
6.2%
c 4506
 
5.8%
f 4308
 
5.5%
n 3526
 
4.5%
i 3253
 
4.2%
s 2440
 
3.1%
Other values (37) 13566
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 15231
19.6%
e 11650
15.0%
a 8260
10.6%
l 6100
7.9%
o 4826
 
6.2%
c 4506
 
5.8%
f 4308
 
5.5%
n 3526
 
4.5%
i 3253
 
4.2%
s 2440
 
3.1%
Other values (37) 13566
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 15231
19.6%
e 11650
15.0%
a 8260
10.6%
l 6100
7.9%
o 4826
 
6.2%
c 4506
 
5.8%
f 4308
 
5.5%
n 3526
 
4.5%
i 3253
 
4.2%
s 2440
 
3.1%
Other values (37) 13566
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 15231
19.6%
e 11650
15.0%
a 8260
10.6%
l 6100
7.9%
o 4826
 
6.2%
c 4506
 
5.8%
f 4308
 
5.5%
n 3526
 
4.5%
i 3253
 
4.2%
s 2440
 
3.1%
Other values (37) 13566
17.5%

Sessions
Real number (ℝ)

High correlation 

Distinct378
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.743995
Minimum1
Maximum1270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-03-18T14:11:18.741804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q312
95-th percentile108
Maximum1270
Range1269
Interquartile range (IQR)11

Descriptive statistics

Standard deviation84.107562
Coefficient of variation (CV)3.267075
Kurtosis59.34075
Mean25.743995
Median Absolute Deviation (MAD)2
Skewness6.8830369
Sum231490
Variance7074.0819
MonotonicityNot monotonic
2025-03-18T14:11:18.774054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2722
30.3%
2 1204
13.4%
3 745
 
8.3%
4 568
 
6.3%
5 355
 
3.9%
6 278
 
3.1%
7 226
 
2.5%
8 195
 
2.2%
9 154
 
1.7%
10 145
 
1.6%
Other values (368) 2400
26.7%
ValueCountFrequency (%)
1 2722
30.3%
2 1204
13.4%
3 745
 
8.3%
4 568
 
6.3%
5 355
 
3.9%
6 278
 
3.1%
7 226
 
2.5%
8 195
 
2.2%
9 154
 
1.7%
10 145
 
1.6%
ValueCountFrequency (%)
1270 1
< 0.1%
1140 1
< 0.1%
1111 1
< 0.1%
1079 1
< 0.1%
1051 1
< 0.1%
1003 1
< 0.1%
985 1
< 0.1%
977 1
< 0.1%
974 1
< 0.1%
972 1
< 0.1%

Total users
Real number (ℝ)

High correlation 

Distinct385
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.650467
Minimum1
Maximum1198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-03-18T14:11:18.805163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile99
Maximum1198
Range1197
Interquartile range (IQR)9

Descriptive statistics

Standard deviation80.617044
Coefficient of variation (CV)3.4086872
Kurtosis61.383779
Mean23.650467
Median Absolute Deviation (MAD)2
Skewness7.0328519
Sum212665
Variance6499.1078
MonotonicityNot monotonic
2025-03-18T14:11:18.837818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3202
35.6%
2 1183
 
13.2%
3 728
 
8.1%
4 470
 
5.2%
5 337
 
3.7%
6 256
 
2.8%
7 220
 
2.4%
8 160
 
1.8%
9 150
 
1.7%
11 98
 
1.1%
Other values (375) 2188
24.3%
ValueCountFrequency (%)
1 3202
35.6%
2 1183
 
13.2%
3 728
 
8.1%
4 470
 
5.2%
5 337
 
3.7%
6 256
 
2.8%
7 220
 
2.4%
8 160
 
1.8%
9 150
 
1.7%
10 94
 
1.0%
ValueCountFrequency (%)
1198 1
< 0.1%
1100 1
< 0.1%
1073 1
< 0.1%
1005 1
< 0.1%
983 1
< 0.1%
971 1
< 0.1%
965 1
< 0.1%
960 1
< 0.1%
957 1
< 0.1%
954 1
< 0.1%

Investimento
Text

Missing 

Distinct649
Distinct (%)23.3%
Missing6208
Missing (%)69.0%
Memory size348.4 KiB
2025-03-18T14:11:18.930673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.7298851
Min length7

Characters and Unicode

Total characters21520
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)7.2%

Sample

1st rowR$ 31,83
2nd rowR$ 31,83
3rd rowR$ 31,83
4th rowR$ 0,00
5th rowR$ 0,00
ValueCountFrequency (%)
r 2784
50.0%
0,00 1343
24.1%
37,56 13
 
0.2%
17,67 11
 
0.2%
7,85 9
 
0.2%
20,37 8
 
0.1%
37,62 8
 
0.1%
41,15 8
 
0.1%
37,02 8
 
0.1%
28,69 7
 
0.1%
Other values (640) 1369
24.6%
2025-03-18T14:11:19.049286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4554
21.2%
R 2784
12.9%
$ 2784
12.9%
2784
12.9%
, 2784
12.9%
1 837
 
3.9%
3 707
 
3.3%
2 706
 
3.3%
7 663
 
3.1%
5 649
 
3.0%
Other values (5) 2268
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4554
21.2%
R 2784
12.9%
$ 2784
12.9%
2784
12.9%
, 2784
12.9%
1 837
 
3.9%
3 707
 
3.3%
2 706
 
3.3%
7 663
 
3.1%
5 649
 
3.0%
Other values (5) 2268
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4554
21.2%
R 2784
12.9%
$ 2784
12.9%
2784
12.9%
, 2784
12.9%
1 837
 
3.9%
3 707
 
3.3%
2 706
 
3.3%
7 663
 
3.1%
5 649
 
3.0%
Other values (5) 2268
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4554
21.2%
R 2784
12.9%
$ 2784
12.9%
2784
12.9%
, 2784
12.9%
1 837
 
3.9%
3 707
 
3.3%
2 706
 
3.3%
7 663
 
3.1%
5 649
 
3.0%
Other values (5) 2268
10.5%

Join_WhatsApp
Real number (ℝ)

High correlation  Zeros 

Distinct193
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6872776
Minimum0
Maximum439
Zeros7237
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-03-18T14:11:19.079091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum439
Range439
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.27844
Coefficient of variation (CV)5.8196767
Kurtosis85.601987
Mean4.6872776
Median Absolute Deviation (MAD)0
Skewness8.6034403
Sum42148
Variance744.11329
MonotonicityNot monotonic
2025-03-18T14:11:19.108678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7237
80.5%
1 702
 
7.8%
2 194
 
2.2%
3 113
 
1.3%
4 80
 
0.9%
5 59
 
0.7%
6 49
 
0.5%
7 33
 
0.4%
8 31
 
0.3%
12 26
 
0.3%
Other values (183) 468
 
5.2%
ValueCountFrequency (%)
0 7237
80.5%
1 702
 
7.8%
2 194
 
2.2%
3 113
 
1.3%
4 80
 
0.9%
5 59
 
0.7%
6 49
 
0.5%
7 33
 
0.4%
8 31
 
0.3%
9 18
 
0.2%
ValueCountFrequency (%)
439 1
< 0.1%
432 1
< 0.1%
384 1
< 0.1%
376 1
< 0.1%
363 1
< 0.1%
360 1
< 0.1%
358 1
< 0.1%
342 1
< 0.1%
335 2
< 0.1%
332 1
< 0.1%

Leads
Real number (ℝ)

High correlation  Zeros 

Distinct216
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3801157
Minimum0
Maximum515
Zeros7331
Zeros (%)81.5%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-03-18T14:11:19.137884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum515
Range515
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32.810424
Coefficient of variation (CV)6.0984608
Kurtosis85.908026
Mean5.3801157
Median Absolute Deviation (MAD)0
Skewness8.6599959
Sum48378
Variance1076.5239
MonotonicityNot monotonic
2025-03-18T14:11:19.167806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7331
81.5%
1 681
 
7.6%
2 215
 
2.4%
3 107
 
1.2%
4 66
 
0.7%
5 59
 
0.7%
6 38
 
0.4%
7 28
 
0.3%
8 27
 
0.3%
9 19
 
0.2%
Other values (206) 421
 
4.7%
ValueCountFrequency (%)
0 7331
81.5%
1 681
 
7.6%
2 215
 
2.4%
3 107
 
1.2%
4 66
 
0.7%
5 59
 
0.7%
6 38
 
0.4%
7 28
 
0.3%
8 27
 
0.3%
9 19
 
0.2%
ValueCountFrequency (%)
515 1
< 0.1%
514 1
< 0.1%
461 1
< 0.1%
447 1
< 0.1%
438 1
< 0.1%
437 1
< 0.1%
432 1
< 0.1%
413 1
< 0.1%
398 1
< 0.1%
393 1
< 0.1%

Vendas
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.076623665
Minimum0
Maximum23
Zeros8568
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-03-18T14:11:19.190544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54781457
Coefficient of variation (CV)7.1494174
Kurtosis586.64233
Mean0.076623665
Median Absolute Deviation (MAD)0
Skewness19.605462
Sum689
Variance0.3001008
MonotonicityNot monotonic
2025-03-18T14:11:19.209509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 8568
95.3%
1 323
 
3.6%
2 56
 
0.6%
3 17
 
0.2%
4 9
 
0.1%
5 6
 
0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%
16 2
 
< 0.1%
10 2
 
< 0.1%
Other values (3) 4
 
< 0.1%
ValueCountFrequency (%)
0 8568
95.3%
1 323
 
3.6%
2 56
 
0.6%
3 17
 
0.2%
4 9
 
0.1%
5 6
 
0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
23 1
 
< 0.1%
16 2
 
< 0.1%
13 1
 
< 0.1%
10 2
 
< 0.1%
8 2
 
< 0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%
5 6
 
0.1%
4 9
0.1%
3 17
0.2%

Receita
Real number (ℝ)

High correlation  Zeros 

Distinct146
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4772239
Minimum0
Maximum998
Zeros8568
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-03-18T14:11:19.235431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum998
Range998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation75.196052
Coefficient of variation (CV)10.056681
Kurtosis144.74835
Mean7.4772239
Median Absolute Deviation (MAD)0
Skewness11.813897
Sum67235.197
Variance5654.4463
MonotonicityNot monotonic
2025-03-18T14:11:19.265547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8568
95.3%
2.4 38
 
0.4%
1.497 31
 
0.3%
2 29
 
0.3%
997 28
 
0.3%
2.988 15
 
0.2%
125 12
 
0.1%
1.69 12
 
0.1%
1.79 12
 
0.1%
1.99 11
 
0.1%
Other values (136) 236
 
2.6%
ValueCountFrequency (%)
0 8568
95.3%
1 1
 
< 0.1%
1.056 1
 
< 0.1%
1.097 1
 
< 0.1%
1.102 2
 
< 0.1%
1.165 1
 
< 0.1%
1.173 3
 
< 0.1%
1.174 1
 
< 0.1%
1.197 1
 
< 0.1%
1.2 1
 
< 0.1%
ValueCountFrequency (%)
998 7
 
0.1%
997 28
0.3%
993 2
 
< 0.1%
897 3
 
< 0.1%
896 2
 
< 0.1%
886 1
 
< 0.1%
799 2
 
< 0.1%
760 1
 
< 0.1%
738 2
 
< 0.1%
729 1
 
< 0.1%

Leads Tráfego Pago
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct164
Distinct (%)16.1%
Missing7972
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean36.232353
Minimum0
Maximum515
Zeros703
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-03-18T14:11:19.294322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile263.25
Maximum515
Range515
Interquartile range (IQR)1

Descriptive statistics

Standard deviation87.982188
Coefficient of variation (CV)2.428277
Kurtosis7.2781792
Mean36.232353
Median Absolute Deviation (MAD)0
Skewness2.7389116
Sum36957
Variance7740.8655
MonotonicityNot monotonic
2025-03-18T14:11:19.324127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 703
 
7.8%
1 67
 
0.7%
2 29
 
0.3%
3 7
 
0.1%
4 5
 
0.1%
129 4
 
< 0.1%
127 4
 
< 0.1%
117 3
 
< 0.1%
145 3
 
< 0.1%
38 3
 
< 0.1%
Other values (154) 192
 
2.1%
(Missing) 7972
88.7%
ValueCountFrequency (%)
0 703
7.8%
1 67
 
0.7%
2 29
 
0.3%
3 7
 
0.1%
4 5
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
12 3
 
< 0.1%
ValueCountFrequency (%)
515 1
< 0.1%
514 1
< 0.1%
461 1
< 0.1%
447 1
< 0.1%
438 1
< 0.1%
437 1
< 0.1%
432 1
< 0.1%
413 1
< 0.1%
398 1
< 0.1%
393 1
< 0.1%

Rank
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.2 KiB
-
8992 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8992
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 8992
100.0%

Length

2025-03-18T14:11:19.348901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-18T14:11:19.361868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8992
100.0%

Most occurring characters

ValueCountFrequency (%)
- 8992
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 8992
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 8992
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 8992
100.0%

Interactions

2025-03-18T14:11:17.083637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.727906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.926430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.120144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.311911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.532065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.716550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.898196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.106521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.760430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.951909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.143805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.334519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.555189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.738216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.921803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.130445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.786988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.975873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.168917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.357824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.578448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.761836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.945013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.153934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.811959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.000916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.193074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.381807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.602800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.785445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.969546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.177768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.834178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.024637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.217558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.403196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.625219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.808004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.992145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.201495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.856989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.048483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.240851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.464340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.648273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.830093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.015699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.226263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.878580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.072015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.264489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.486119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.670095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.852223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.038059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.249076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:15.902018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.095479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.287623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.509459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.692621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:16.874243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-18T14:11:17.061324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-18T14:11:19.377497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Ano/MesCanalCategoriaFaseJoin_WhatsAppLeadsLeads Tráfego PagoObjetivoOrganizadorReceitaSessionsTotal usersVendas
Ano/Mes1.0000.1000.1330.8080.0460.0480.1750.1150.8770.0710.0420.0440.015
Canal0.1001.0000.9990.1040.1380.1380.2170.4550.1000.0630.1340.1340.023
Categoria0.1330.9991.0000.1350.1600.1601.0000.4010.1000.0510.1280.1290.000
Fase0.8080.1040.1351.0000.0900.0930.3080.1070.8430.0980.0790.0830.086
Join_WhatsApp0.0460.1380.1600.0901.0000.8660.9510.386-0.0010.0600.4420.4490.061
Leads0.0480.1380.1600.0930.8661.0001.0000.3860.0480.0480.4250.4350.049
Leads Tráfego Pago0.1750.2171.0000.3080.9511.0001.0000.3610.135-0.0340.6240.633-0.035
Objetivo0.1150.4550.4010.1070.3860.3860.3611.0000.1130.0000.3100.3130.000
Organizador0.8770.1000.1000.843-0.0010.0480.1350.1131.000-0.0340.0870.095-0.034
Receita0.0710.0630.0510.0980.0600.048-0.0340.000-0.0341.0000.2070.2040.999
Sessions0.0420.1340.1280.0790.4420.4250.6240.3100.0870.2071.0000.9700.210
Total users0.0440.1340.1290.0830.4490.4350.6330.3130.0950.2040.9701.0000.206
Vendas0.0150.0230.0000.0860.0610.049-0.0350.000-0.0340.9990.2100.2061.000

Missing values

2025-03-18T14:11:17.331719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-18T14:11:17.381932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-18T14:11:17.564174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OrganizadorAno/MesConcat2CategoriaCanalConcatSemanaFaseObjetivoDateSession sourceSession mediumSessionsTotal usersInvestimentoJoin_WhatsAppLeadsVendasReceitaLeads Tráfego PagoRank
012024 | JaneiroTráfego Direto45307Tráfego OrgânicoTráfego Direto(direct)(none)2024 | 15/01 - 21/01Ongoing-2024-01-16(direct)(none)22NaN0000.0NaN-
122024 | JaneiroTráfego Direto45308Tráfego OrgânicoTráfego Direto(direct)(none)2024 | 15/01 - 21/01Ongoing-2024-01-17(direct)(none)4126NaN0000.0NaN-
232024 | JaneiroMeta Ads45308Tráfego PagoMeta Adsfacebooklinktext2024 | 15/01 - 21/01Ongoing-2024-01-17facebooklinktext2827R$ 31,830000.00.0-
342024 | JaneiroReferral45308Tráfego OrgânicoReferralmy.orbitpages.comreferral2024 | 15/01 - 21/01Ongoing-2024-01-17my.orbitpages.comreferral42NaN0000.0NaN-
452024 | JaneiroMeta Ads45308Tráfego PagoMeta AdsfacebookFacebook_Right_Column2024 | 15/01 - 21/01Ongoing-2024-01-17facebookFacebook_Right_Column33R$ 31,830000.00.0-
562024 | JaneiroReferral45308Tráfego OrgânicoReferralfacebook.comreferral2024 | 15/01 - 21/01Ongoing-2024-01-17facebook.comreferral33NaN0000.0NaN-
672024 | JaneiroMeta Ads45308Tráfego PagoMeta Adsfacebook{{placement}}2024 | 15/01 - 21/01Ongoing-2024-01-17facebook{{placement}}22R$ 31,830000.00.0-
782024 | JaneiroReferral45308Tráfego OrgânicoReferral(not set)(not set)2024 | 15/01 - 21/01Ongoing-2024-01-17(not set)(not set)11NaN0000.0NaN-
892024 | JaneiroReferral45308Tráfego OrgânicoReferraladsmanager.facebook.comreferral2024 | 15/01 - 21/01Ongoing-2024-01-17adsmanager.facebook.comreferral11NaN0000.0NaN-
9102024 | JaneiroReferral45308Tráfego OrgânicoReferralbusiness.facebook.comreferral2024 | 15/01 - 21/01Ongoing-2024-01-17business.facebook.comreferral11NaN0000.0NaN-
OrganizadorAno/MesConcat2CategoriaCanalConcatSemanaFaseObjetivoDateSession sourceSession mediumSessionsTotal usersInvestimentoJoin_WhatsAppLeadsVendasReceitaLeads Tráfego PagoRank
898289832025 | MarçoYoutube45732Social OrgânicoYoutubeyoutube.comreferral2025 | 10/03 - 16/0314º Lançamento | Leads-2025-03-16youtube.comreferral97NaN0100.0NaN-
898389842025 | MarçoWhatsApp45732Social OrgânicoWhatsAppwhatsappgrupos-descricao2025 | 10/03 - 16/0314º Lançamento | Leads-2025-03-16whatsappgrupos-descricao32NaN0000.0NaN-
898489852025 | MarçoReferral45732Tráfego OrgânicoReferraljornadadedados.alpaclass.comreferral2025 | 10/03 - 16/0314º Lançamento | Leads-2025-03-16jornadadedados.alpaclass.comreferral21NaN1000.0NaN-
898589862025 | MarçoReferral45732Tráfego OrgânicoReferralsearch.brave.comreferral2025 | 10/03 - 16/0314º Lançamento | Leads-2025-03-16search.brave.comreferral22NaN0000.0NaN-
898689872025 | MarçoWhatsApp45732Social OrgânicoWhatsAppwhatsappdirect2025 | 10/03 - 16/0314º Lançamento | Leads-2025-03-16whatsappdirect22NaN0000.0NaN-
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